"""simple docstring"""



import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[int]							  =						GPTaTokenizer
 _snake_case       :					Optional[int]							  =						GPTaTokenizerFast
 _snake_case       :					Dict							  =						True
 _snake_case       :					Tuple							  =						{"""add_prefix_space""": True}
 _snake_case       :					Optional[int]							  =						False


 def 						a__   ( self						):
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __a       =    [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
            "<|endoftext|>",
        ]
        __a       =    dict(zip(lowerCamelCase    ,		range(len(lowerCamelCase						)						)						)						)
        __a       =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        __a       =    {"unk_token": "<unk>"}

        __a       =    os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES["vocab_file"]						)
        __a       =    os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES["merges_file"]						)
        with open(self.vocab_file    ,		"w"    ,		encoding="utf-8"						) as fp:
               fp.write(json.dumps(lowerCamelCase						) + "\n"						)
        with open(self.merges_file    ,		"w"    ,		encoding="utf-8"						) as fp:
               fp.write("\n".join(lowerCamelCase						)						)


 def 						a__   ( self    ,		**lowerCamelCase						):
        kwargs.update(self.special_tokens_map						)
        return GPTaTokenizer.from_pretrained(self.tmpdirname    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		**lowerCamelCase						):
        kwargs.update(self.special_tokens_map						)
        return GPTaTokenizerFast.from_pretrained(self.tmpdirname    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    "lower newer"
        __a       =    "lower newer"
        return input_text, output_text


 def 						a__   ( self						):
        __a       =    GPTaTokenizer(self.vocab_file    ,		self.merges_file    ,		**self.special_tokens_map						)
        __a       =    "lower newer"
        __a       =    ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
        __a       =    tokenizer.tokenize(lowerCamelCase    ,		add_prefix_space=lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    tokens + [tokenizer.unk_token]
        __a       =    [14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase						)    ,		lowerCamelCase						)


 def 						a__   ( self						):
        if not self.test_rust_tokenizer:
               return

        __a       =    self.get_tokenizer()
        __a       =    self.get_rust_tokenizer(add_prefix_space=lowerCamelCase						)

        __a       =    "lower newer"

        # Testing tokenization
        __a       =    tokenizer.tokenize(lowerCamelCase    ,		add_prefix_space=lowerCamelCase						)
        __a       =    rust_tokenizer.tokenize(lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)

        # Testing conversion to ids without special tokens
        __a       =    tokenizer.encode(lowerCamelCase    ,		add_special_tokens=lowerCamelCase    ,		add_prefix_space=lowerCamelCase						)
        __a       =    rust_tokenizer.encode(lowerCamelCase    ,		add_special_tokens=lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)

        # Testing conversion to ids with special tokens
        __a       =    self.get_rust_tokenizer(add_prefix_space=lowerCamelCase						)
        __a       =    tokenizer.encode(lowerCamelCase    ,		add_prefix_space=lowerCamelCase						)
        __a       =    rust_tokenizer.encode(lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)

        # Testing the unknown token
        __a       =    tokens + [rust_tokenizer.unk_token]
        __a       =    [14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCamelCase						)    ,		lowerCamelCase						)


 def 						a__   ( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        # It's very difficult to mix/test pretokenization with byte-level
        # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
        pass


 def 						a__   ( self    ,		lowerCamelCase=15						):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
                      __a       =    self.rust_tokenizer_class.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)

                      # Simple input
                      __a       =    "This is a simple input"
                      __a       =    ["This is a simple input 1", "This is a simple input 2"]
                      __a       =    ("This is a simple input", "This is a pair")
                      __a       =    [
                          ("This is a simple input 1", "This is a simple input 2"),
                          ("This is a simple pair 1", "This is a simple pair 2"),
                      ]

                      # Simple input tests
                      self.assertRaises(lowerCamelCase    ,		tokenizer_r.encode    ,		lowerCamelCase    ,		max_length=lowerCamelCase    ,		padding="max_length"						)

                      # Simple input
                      self.assertRaises(lowerCamelCase    ,		tokenizer_r.encode_plus    ,		lowerCamelCase    ,		max_length=lowerCamelCase    ,		padding="max_length"						)

                      # Simple input
                      self.assertRaises(
                          lowerCamelCase    ,		tokenizer_r.batch_encode_plus    ,		lowerCamelCase    ,		max_length=lowerCamelCase    ,		padding="max_length"    ,		)

                      # Pair input
                      self.assertRaises(lowerCamelCase    ,		tokenizer_r.encode    ,		lowerCamelCase    ,		max_length=lowerCamelCase    ,		padding="max_length"						)

                      # Pair input
                      self.assertRaises(lowerCamelCase    ,		tokenizer_r.encode_plus    ,		lowerCamelCase    ,		max_length=lowerCamelCase    ,		padding="max_length"						)

                      # Pair input
                      self.assertRaises(
                          lowerCamelCase    ,		tokenizer_r.batch_encode_plus    ,		lowerCamelCase    ,		max_length=lowerCamelCase    ,		padding="max_length"    ,		)


 def 						a__   ( self						):
        __a       =    GPTaTokenizer.from_pretrained(self.tmpdirname    ,		pad_token="<pad>"						)

        # Simple input
        __a       =    "This is a simple input"
        __a       =    ["This is a simple input looooooooong", "This is a simple input"]
        __a       =    ("This is a simple input", "This is a pair")
        __a       =    [
            ("This is a simple input loooooong", "This is a simple input"),
            ("This is a simple pair loooooong", "This is a simple pair"),
        ]

        __a       =    tokenizer.pad_token_id

        __a       =    tokenizer(lowerCamelCase    ,		padding="max_length"    ,		max_length=30    ,		return_tensors="np"						)
        __a       =    tokenizer(lowerCamelCase    ,		padding=lowerCamelCase    ,		truncate=lowerCamelCase    ,		return_tensors="np"						)
        __a       =    tokenizer(*lowerCamelCase    ,		padding="max_length"    ,		max_length=60    ,		return_tensors="np"						)
        __a       =    tokenizer(lowerCamelCase    ,		padding=lowerCamelCase    ,		truncate=lowerCamelCase    ,		return_tensors="np"						)

        # s
        # test single string max_length padding
        self.assertEqual(out_s["input_ids"].shape[-1]    ,		30						)
        self.assertTrue(pad_token_id in out_s["input_ids"]						)
        self.assertTrue(0 in out_s["attention_mask"]						)

        # s2
        # test automatic padding
        self.assertEqual(out_sa["input_ids"].shape[-1]    ,		33						)
        # long slice doesn't have padding
        self.assertFalse(pad_token_id in out_sa["input_ids"][0]						)
        self.assertFalse(0 in out_sa["attention_mask"][0]						)
        # short slice does have padding
        self.assertTrue(pad_token_id in out_sa["input_ids"][1]						)
        self.assertTrue(0 in out_sa["attention_mask"][1]						)

        # p
        # test single pair max_length padding
        self.assertEqual(out_p["input_ids"].shape[-1]    ,		60						)
        self.assertTrue(pad_token_id in out_p["input_ids"]						)
        self.assertTrue(0 in out_p["attention_mask"]						)

        # p2
        # test automatic padding pair
        self.assertEqual(out_pa["input_ids"].shape[-1]    ,		52						)
        # long slice pair doesn't have padding
        self.assertFalse(pad_token_id in out_pa["input_ids"][0]						)
        self.assertFalse(0 in out_pa["attention_mask"][0]						)
        # short slice pair does have padding
        self.assertTrue(pad_token_id in out_pa["input_ids"][1]						)
        self.assertTrue(0 in out_pa["attention_mask"][1]						)


 def 						a__   ( self						):
        __a       =    "$$$"
        __a       =    GPTaTokenizer.from_pretrained(self.tmpdirname    ,		bos_token=lowerCamelCase    ,		add_bos_token=lowerCamelCase						)

        __a       =    "This is a simple input"
        __a       =    ["This is a simple input 1", "This is a simple input 2"]

        __a       =    tokenizer.bos_token_id

        __a       =    tokenizer(lowerCamelCase						)
        __a       =    tokenizer(lowerCamelCase						)

        self.assertEqual(out_s.input_ids[0]    ,		lowerCamelCase						)
        self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

        __a       =    tokenizer.decode(out_s.input_ids						)
        __a       =    tokenizer.batch_decode(out_sa.input_ids						)

        self.assertEqual(decode_s.split()[0]    ,		lowerCamelCase						)
        self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)


 def 						a__   ( self						):
        pass






 def 						a__   ( self						):
        # TODO: change to self.get_tokenizers() when the fast version is implemented
        __a       =    [self.get_tokenizer(do_lower_case=lowerCamelCase    ,		add_bos_token=lowerCamelCase						)]
        for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"						):
                      __a       =    "Encode this."
                      __a       =    "This one too please."
                      __a       =    tokenizer.encode(lowerCamelCase    ,		add_special_tokens=lowerCamelCase						)
                      encoded_sequence += tokenizer.encode(lowerCamelCase    ,		add_special_tokens=lowerCamelCase						)
                      __a       =    tokenizer.encode_plus(
                          lowerCamelCase    ,		lowerCamelCase    ,		add_special_tokens=lowerCamelCase    ,		return_special_tokens_mask=lowerCamelCase    ,		)
                      __a       =    encoded_sequence_dict["input_ids"]
                      __a       =    encoded_sequence_dict["special_tokens_mask"]
                      self.assertEqual(len(lowerCamelCase						)    ,		len(lowerCamelCase						)						)

                      __a       =    [
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCamelCase						)
                      ]
                      __a       =    [x for x in filtered_sequence if x is not None]
                      self.assertEqual(lowerCamelCase    ,		lowerCamelCase						)






@require_tokenizers
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        # More context:
        # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
        # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
        # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

        __a       =    AutoTokenizer.from_pretrained("facebook/opt-350m"    ,		from_slow=lowerCamelCase						)
        __a       =    "A photo of a cat"

        __a       =    tokenizer.encode(
            lowerCamelCase    ,		)
        self.assertEqual(lowerCamelCase    ,		[2, 250, 1345, 9, 10, 4758]						)
        tokenizer.save_pretrained("test_opt"						)

        __a       =    AutoTokenizer.from_pretrained("./test_opt"						)
        __a       =    tokenizer.encode(
            lowerCamelCase    ,		)
        self.assertEqual(lowerCamelCase    ,		[2, 250, 1345, 9, 10, 4758]						)


 def 						a__   ( self						):
        __a       =    AutoTokenizer.from_pretrained("facebook/opt-350m"    ,		use_slow=lowerCamelCase						)
        __a       =    "A photo of a cat"

        __a       =    tokenizer.encode(
            lowerCamelCase    ,		)
        # Same as above
        self.assertEqual(lowerCamelCase    ,		[2, 250, 1345, 9, 10, 4758]						)






 @unittest.skip("This test is failing because of a bug in the fast tokenizer"						)
 def 						a__   ( self						):
        __a       =    AutoTokenizer.from_pretrained("facebook/opt-350m"    ,		from_slow=lowerCamelCase						)

        __a       =    "bos"
        __a       =    tokenizer.get_vocab()["bos"]

        __a       =    "A photo of a cat"
        __a       =    tokenizer.encode(
            lowerCamelCase    ,		)
        # We changed the bos token
        self.assertEqual(lowerCamelCase    ,		[31957, 250, 1345, 9, 10, 4758]						)
        tokenizer.save_pretrained("./tok"						)
        __a       =    AutoTokenizer.from_pretrained("./tok"						)
        self.assertTrue(tokenizer.is_fast						)
        __a       =    tokenizer.encode(
            lowerCamelCase    ,		)
        self.assertEqual(lowerCamelCase    ,		[31957, 250, 1345, 9, 10, 4758]						)

"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
